Evaluating a language model is a lot like reviewing how a digital platform performs after launch. You wouldn’t just measure if pages load quickly, but also whether users can find what they need easily, the design feels intuitive, and the experience reflects the brand. The same thinking applies here.
Evaluation means understanding how well the model communicates, adapts to context, and supports real business outcomes.
Measuring fluency, coherence, relevance, and creativity helps ensure every response is purposeful, accurate, and aligned with what teams and clients actually need.
1. Fluency
Fluency is the smoothness and readability of the generated text. It assesses whether the output flows naturally and adheres to grammatical rules, making it easy for users to read and understand. A fluent response should be free of awkward phrasing and maintain a consistent tone throughout.
Here is the list of fluency-based LLM Metrics that can be used to evaluate our LLM for content generation
1.1 G-Eval
G-Eval (short for Generative Evaluation) is a human-centric approach for assessing the output of language models. Instead of relying solely on automated scores, G-Eval typically involves human judges evaluating how well the model's responses align with specific goals such as relevance, coherence, accuracy, and creativity. In a chatbot or AI assistant context, G-Eval can be used to evaluate how helpful, engaging, or accurate the AI’s responses are to users.
Strengths:
- Human-oriented: Since humans evaluate the responses, it captures subjective factors like quality, tone, and helpfulness, which are difficult to measure using automated metrics.
- Comprehensive: It considers multiple aspects like relevance, fluency, creativity, and user satisfaction, offering a more well-rounded evaluation.
- Contextual evaluation: G-Eval can adapt to different tasks and assess the model based on the specific goals of the application (e.g., customer support vs. creative writing).
Weaknesses:
- Time-consuming: Since it requires human reviewers, G-Eval can be slow and expensive compared to automated metrics.
- Subjectivity: Different people may evaluate the same response differently, which could introduce bias or inconsistencies in the results.
- Scalability: It’s not easy to scale, especially when evaluating large datasets or in real-time systems.
G-Eval gives a detailed and human-centric evaluation of an AI’s performance, making it very useful for tasks where quality and user satisfaction are key, but it is slower and more subjective than automated methods.
1.2 Summarisation
Summarisation as a metric involves evaluating how well a language model can condense large amounts of information into a shorter form while maintaining the key points. In the context of AI chatbots, summarisation could be used to evaluate how effectively the AI can take a long conversation or complex topic and provide a brief, clear summary that captures the essence.
Strengths:
- Efficiency: Summarisation allows users to quickly understand long pieces of information.
- Quality indicator: How well an AI summarises a topic can give insights into its understanding of the content.
- Practical use case: Summarisation is valuable for real-world applications like news briefings, customer support recaps, or summarising property-related details for our use case.
Weaknesses:
- Loss of detail: Summarisation can sometimes omit important details or context, leading to oversimplification.
- Coherence issues: If the model doesn’t understand the content well, the summary can be unclear or misleading.
- Difficult to evaluate: Judging whether a summary is good can be subjective, as different people may focus on different key points.
Summarisation helps to evaluate an AI’s ability to condense and understand content, but the risk lies in losing important details or generating confusing summaries.
1.3 Toxicity
Toxicity as a metric is used to evaluate how often a language model generates harmful, offensive, or inappropriate content. In AI chatbot contexts, this metric is critical for ensuring that the AI remains respectful, unbiased, and appropriate in its responses, particularly when interacting with users on sensitive topics.
Strengths:
- User safety: Evaluating toxicity helps prevent harmful or offensive language, ensuring a safer and more inclusive user experience.
- Ethical AI: Reduces the chance of spreading harmful biases or toxic content, contributing to responsible AI deployment.
- Measurable: There are automated tools and algorithms (like OpenAI’s content filters) that can detect toxic content quickly and at scale.
Weaknesses:
- False positives: Sometimes, benign content can be flagged as toxic (overly sensitive filtering), which could result in censorship of valid or important discussions.
- False negatives: The model might fail to detect more subtle toxic behaviours like sarcasm, passive aggression, or coded language.
- Context-dependence: Whether content is toxic can depend on context, tone, and cultural sensitivity, making it difficult to evaluate purely with automated systems.
Toxicity evaluation is key to ensuring AI behaves ethically and safely, but the challenges lie in balancing accurate detection with avoiding overly harsh censorship.
1.4 Bias
Bias as a metric refers to evaluating whether a language model treats different groups (such as people based on race, gender, age, or religion) unfairly or shows a preference toward certain viewpoints or stereotypes. In AI chatbots, bias can appear in the form of biased responses or assumptions in the conversation, often reflecting societal or data-driven biases that were unintentionally learned during training.
Strengths:
- Fairness: Evaluating bias ensures the AI treats all users and topics fairly and respectfully, without showing discrimination or favouritism.
- Ethical AI: Helps in aligning the model’s behaviour with societal norms and ethical guidelines by minimising harmful stereotypes or biased language.
- Automated Detection: There are tools and methods to automatically detect bias across different dimensions, such as gender or racial bias, making it scalable.
Weaknesses:
- Hard to Define: Bias can be subjective and depends heavily on cultural, social, and regional norms, making it challenging to universally define or detect.
- Complex to Measure: Bias is multidimensional (gender, racial, ideological, etc.) and may appear subtly, requiring extensive checks and diverse data to detect it accurately.
- False Neutrality: In trying to eliminate bias, models might become too neutral or overly cautious, avoiding important discussions or appearing bland and disengaged.
In short, bias evaluation ensures the AI interacts fairly and ethically with users, but it is challenging to define and detect, especially because bias can be subtle and context-dependent.
2. Coherence
Coherence measures how logically connected and understandable the generated content is. It evaluates whether ideas are presented in a clear, organised manner, allowing the reader to follow the argument or narrative without confusion. A coherent response effectively relates its components to form a unified message.
2.1 Conversation completeness
Conversation completeness measures how well a language model (like an AI chatbot) provides a full, coherent, and satisfying response or solution to a user's query. It evaluates whether the AI addressed all parts of the user’s request, avoided leaving gaps in the conversation, and provided a conclusion that feels complete.
Strengths:
- User satisfaction: Ensures that users get complete and well-rounded responses, reducing frustration from incomplete or partial answers.
- Task accomplishment: Especially important in goal-oriented interactions (like customer support or technical troubleshooting), where a complete conversation is necessary to resolve issues.
- Efficiency: Helps ensure users don't have to ask multiple follow-up questions, saving time and improving the overall interaction quality.
Weaknesses:
- Difficult to automate: Automated systems may struggle to detect when a conversation is incomplete, especially in complex interactions.
- Overloading responses: Trying to be overly complete may lead to responses that are too long or filled with unnecessary information, which can overwhelm users.
- Context sensitivity: What constitutes "complete" can vary based on the user's intent and the complexity of the query. A response that feels complete to one person may not feel that way to another.
Conversation completeness ensures the AI provides thorough and satisfactory answers, but the challenge lies in balancing detailed responses with conciseness and user expectations.
2.2 Knowledge retention
Knowledge retention refers to how well a language model (like an AI chatbot) can remember and use information from earlier parts of a conversation throughout the interaction. It measures the AI’s ability to "retain" context and facts provided by the user and incorporate them correctly in later responses.
Strengths:
- Context awareness: Helps ensure the AI delivers coherent, contextually relevant answers by remembering key facts from earlier in the conversation.
- Natural conversations: Makes the interaction feel more human-like, as users don’t have to repeat themselves, leading to smoother, more efficient dialogues.
- Consistency: Improves the consistency of responses, as the AI can build on previously discussed topics rather than starting fresh with every reply.
Weaknesses:
- Limited memory: Many models struggle with long conversations, leading to a loss of earlier context over time (especially in extended chats).
- Over-reliance: If the AI remembers irrelevant details or misunderstandings from earlier, it may apply them incorrectly in future responses, confusing.
- Performance impact: Keeping track of a large amount of conversational data can be computationally expensive and may affect the system’s performance or lead to slower response times.
Knowledge retention ensures that the AI remembers relevant information throughout the conversation, making interactions smoother and more coherent, but challenges arise with long conversations and maintaining relevant context.
3. Relevance
Relevance evaluates how well the generated content addresses the user’s query or aligns with the context of the conversation. It assesses the appropriateness of the information provided and its significance to the specific topic at hand. Relevant responses ensure that users receive meaningful and contextually appropriate information.
3.1 Answer relevance
Answer relevancy measures how closely a language model's response aligns with the user's question or request. It evaluates whether the AI provides an answer that is directly related to the query and addresses the user’s intent without deviating into unrelated or unnecessary topics.
Strengths:
- User satisfaction: Relevant answers improve user satisfaction by providing exactly what the user is looking for, without confusion or irrelevant information.
- Efficiency: By sticking to relevant answers, the AI reduces the need for users to ask follow-up questions or clarify their requests, making the interaction more efficient.
- Task-oriented: In applications like customer support or information retrieval, relevance ensures the AI provides actionable and useful responses that help users achieve their goals.
Weaknesses:
- Context sensitivity: Determining relevance can be tricky, especially in ambiguous or complex queries where the user's intent isn’t clear, leading to potentially inaccurate or incomplete answers.
- Over-simplification: Focusing too much on staying relevant might cause the AI to oversimplify responses, missing nuanced or valuable information that could be useful.
- Limited creativity: High relevance can sometimes prevent the AI from providing creative or broader responses that might enrich the conversation or offer unexpected insights.
Answer relevancy ensures the AI provides responses that are on-point and directly related to user queries, but challenges include understanding ambiguous intent and balancing relevance with depth or creativity.
3.2 Contextual relevancy
Contextual relevance measures how well a language model maintains awareness of the ongoing conversation's context when generating responses. It evaluates whether the AI responds in a way that is not only relevant to the user’s most recent input but also aligned with the broader conversation history, ensuring continuity and coherence.
Strengths:
- Natural conversation flow: Helps the AI deliver responses that make sense in the context of the conversation, creating a more fluid and human-like interaction.
- Context retention: Ensures the AI can build on previously discussed topics and doesn’t lose track of information shared earlier, which makes the interaction more cohesive.
- Improves user experience: By staying contextually relevant, the AI avoids misunderstandings and provides answers that reflect the user’s intent over the entire dialogue, improving satisfaction.
Weaknesses:
- Context confusion: If the AI misinterprets or forgets key elements of the previous conversation, it may provide irrelevant or confusing responses.
- Memory limitations: In long conversations, the AI may struggle to retain all prior context, leading to diminishing contextual relevancy over time.
- Balance between current and past inputs: Over-focusing on earlier parts of the conversation might cause the AI to miss important details in the most recent input, leading to outdated or irrelevant answers.
Contextual relevancy ensures the AI’s responses are coherent and make sense within the flow of the entire conversation, but the challenge is in managing long or complex dialogues without losing important context.
4. Creativity
Creativity gauges the originality and inventiveness of the output. It looks at how well the model can generate novel ideas, solutions, or expressions that go beyond rote responses. A creative response showcases the model's ability to think outside the box and present unique perspectives or interpretations.
4.1 Faithfulness
Faithfulness refers to how accurately a language model’s responses stick to the facts, instructions, or source material provided. In AI chatbots, faithfulness ensures that the information shared in responses is factually correct, aligned with the input provided by the user, and doesn’t introduce false or misleading information.
Strengths:
- Accuracy: Faithfulness helps ensure that the AI provides responses that are truthful and grounded in reality, which is critical in applications like customer support, healthcare, or educational tools.
- Trustworthiness: High faithfulness builds user trust by preventing the AI from making up information ("hallucinations") or giving misleading answers.
- Consistency: A faithful model maintains consistency with prior information or instructions given during the conversation, ensuring the AI doesn’t contradict itself.
Weaknesses:
- Creativity limitation: Prioritising faithfulness can sometimes limit the AI’s ability to be creative or to provide imaginative responses, especially in non-factual or open-ended conversations.
- Model hallucination: Language models sometimes generate plausible but incorrect information, which can lead to unfaithful responses, especially when answering complex or unfamiliar topics.
- Hard to measure automatically: Faithfulness can be difficult to measure with automated metrics, as it often requires human judgment or external verification (fact-checking) to ensure the response is accurate and relevant to the input.
Faithfulness ensures the AI gives accurate and reliable answers, but the challenges lie in balancing truthfulness with creativity and preventing the AI from unintentionally generating false or misleading information.
4.2 Hallucination
Hallucination refers to the phenomenon where an AI generates information that is false, misleading, or fabricated, despite sounding plausible or credible. This can occur when the model creates responses that don't accurately reflect the data it was trained on or when it fails to retrieve factual information.
Strengths:
- Identifies limitations: Understanding hallucination helps developers recognise the weaknesses of a model, particularly in its ability to provide accurate information.
- Improves training: Identifying patterns of hallucination can lead to better training strategies, data curation, and model architecture improvements aimed at reducing this issue.
- Awareness of risks: Acknowledging hallucination is crucial for developers and users to set realistic expectations about the reliability of AI-generated content.
Weaknesses:
- Misinformation spread: Hallucination can lead to the spread of misinformation, which can have serious implications, especially in sensitive areas like healthcare, law, or finance.
- User trust: Frequent hallucinations can erode user trust in AI systems, making users sceptical of the information provided.
- Difficult to detect: It can be challenging to identify when hallucination occurs, as the responses may sound convincing and may not be easily verified without external checks.
Hallucination highlights a critical challenge in language models, where the AI generates false or misleading information. While understanding hallucination can improve model development and user awareness, it poses risks in terms of misinformation and user trust.
4.3 RAGAS
RAGAS stands for "Relevance, Adequacy, Granularity, Accuracy, and Specificity." It is a comprehensive metric used to evaluate the quality of responses generated by language models. Each component of RAGAS focuses on different aspects of response quality, ensuring a well-rounded assessment of how well an AI meets user needs.
Strengths:
- Comprehensive assessment: RAGAS provides a multifaceted evaluation of response quality, covering essential dimensions of effective communication.
- User-centric: By focusing on relevance and adequacy, RAGAS prioritises user satisfaction and needs.
- Flexibility: Can be applied to a variety of applications, from chatbots to content generation, making it a versatile evaluation tool.
Weaknesses:
- Complexity: Evaluating all five components can be time-consuming and may require subjective judgment, particularly in gauging relevance and adequacy.
- Subjectivity: Different users may have varying expectations for specificity and granularity, which can lead to inconsistent evaluations.
- Challenges in Automation: Some components may be difficult to measure using automated systems, necessitating human evaluation for accuracy.
RAGAS serves as a comprehensive framework for evaluating the quality of AI-generated responses, focusing on key aspects that impact user experience. However, it requires careful consideration and possibly human input to assess effectively.
5. Tools to evaluate LLM (Open source)
RAGAS stands for "Relevance, Adequacy, Granularity, Accuracy, and Specificity." It is a comprehensive metric used to evaluate the quality of responses generated by language models. Each component of RAGAS focuses on different aspects of response quality, ensuring a well-rounded assessment of how well an AI meets user needs.
Strengths:
- Comprehensive Assessment: RAGAS provides a multifaceted evaluation of response quality, covering essential dimensions of effective communication.
- User-Centric: By focusing on relevance and adequacy, RAGAS prioritises user satisfaction and needs.
- Flexibility: Can be applied to a variety of applications, from chatbots to content generation, making it a versatile evaluation tool.
Weaknesses:
- Complexity: Evaluating all five components can be time-consuming and may require subjective judgment, particularly in gauging relevance and adequacy.
- Subjectivity: Different users may have varying expectations for specificity and granularity, which can lead to inconsistent evaluations.
- Challenges in Automation: Some components may be difficult to measure using automated systems, necessitating human evaluation for accuracy.
RAGAS serves as a comprehensive framework for evaluating the quality of AI-generated responses, focusing on key aspects that impact user experience. However, it requires careful consideration and possibly human input to assess effectively.
1. DeepEval
Overview: A widely used framework that is user-friendly and flexible.
Key Features:
- Built-in metrics such as GEval, Summarisation, Answer Relevancy, and more.
- Supports custom metric creation.
- Integrates well into CI/CD pipelines.
- Includes benchmark datasets like MMLU and HellaSwag.
2. Giskard
Overview: A Python-based framework designed for evaluating LLMs and detecting issues.
Key Features:
- Identifies performance, bias, and security issues.
- Comes with a RAG Evaluation Toolkit for testing Retrieval Augmented Generation applications.
- Works with various models and environments.
3. TruLens
Overview: Focuses on transparency and interpretability in LLM evaluation.
Key features:
- Allows for the evaluation of LLM outputs using feedback functions like Groundedness and User Sentiment.
- Custom feedback functions can be defined for tailored evaluations.
4. Evals by OpenAI
Overview: An evaluation framework specifically designed for LLMs or applications built on them.
Key features:
- Provides a standardised way to measure model performance.
- Includes an open source registry of challenging evaluations.
5. Evidently
Overview: A Python library that supports evaluations for various LLM applications, including chatbots and RAGs.
Key Features:
- Allows task-specific evaluations with visual reports and automated test suites.
- Integrates seamlessly into existing monitoring dashboards.
6. MLFlow
Overview: A comprehensive platform that supports the entire machine learning lifecycle, including LLM evaluation.
Key features:
- Enables tracking experiments and running evaluations within custom pipelines.